Overview

The aim was to prepare a spatio-temporal representation of valuation studies related to biodiversity and ecosystem services and … .

1 Creation of the Corpus

2 Georeferencing the Corpus

To identify country names in the corpus of literature a two step approach was used. First, we wanted to understand where studies were conducted and searched the title, abstract, and keywords of each paper for country names. Second, to understand where the funding institutions were located we searched the affiliations, acknowledgments, and funding text for country names.

The input data we used are the following:

The python code used to georeference the corpus can be found here. An overview of the pipeline is provided in the following schematic and described below.

knitr::include_graphics("pilot2.svg")
Overview of the process of Georeferencing the corpus of valuation studies

Overview of the process of Georeferencing the corpus of valuation studies

Step 1: Extract country names from text Country names were extracted from the title, abstract, and keywords of each paper with a regular expression and the associated ISO code was added into a a column in the dataset. The same regular expression was also used to search the affiliations, acknowledgments, and funding text of the same paper and placed into a second column.

Step 2 and 3: Bundle countries in regions The IPBES Regions and Subregions datatset was then used to add additional region and subregion attributes to the dataset by matching the ISO3 code.

Step 4: Find TS accordingly Finally, we used a set of files to add additional attributes to the dataset that identified the topics. The set of files contained identifying information for papers derived from sets of web of science searches targeting particular topics. This identifying information was then matched to the corpus, and the topic extracted.

Finally, the complete corpus with the added attributes of country ISO codes of both funding institutions and research locations, and topic identification were used as the basis of the rest of the research project.

3 Indicator Analysis

The IPBES Core Indicators were used alongside a chosen set of other relevant indicators to understand geographic trends between the density of studies and density of institutes.

3.1 Selection of Indicators

3.1.1 IPBES Core Indicators

We used all the most recent year of the IPBES Core Indicators available within the country dataset except for two indicators, Countries/Regions with Active NBSAP and Category 1 nations in CTIES, as these are binary in the dataset and would not be compatible with the following analysis. We selected a specific category from the indicators with multiple categories. For example, for the indicator “Area of forest production under FSC and PEFC certification” we chose the FSC certification area and not the PEFC certification area.

Here is the table of all of the IPBES Core Indicators used, the category selected, the year the data is from, and the number assigned to them.

Table 1: Details of the IPBES Core Indicators Used
Name Category Year Number
Area of forest production under FSC and PEFC certification FSC_area 2016 1
Biodiversity Habitat Index Average 2014 2
Biodiversity Intactness Index Value 2005 3
Biocapacity per capita Value - Total 2012 4
Ecological Footprint per capita Value - Total 2012 5
Forest area Forest area (1000ha) 2015 6
Water Footprint Water Footprint - Total (Mm3/y) 2013 7
Inland Fishery Production Capture 2015 8
Region-based Marine Trophic Index 1950 2014 9
Nitrogen + Phosphate Fertilizers N total nutrients - Consumption in nutrients 2014 10
Nitrogen Use Efficiency (%) Nitrogen Use Efficiency (%) 2009 11
Percentage and total area covered by protected areas Terrestrial - Protected Area (%) 2017 13
Percentage of undernourished people Prevalence of undernourishment (%) (3-year average) 2015 15
Proportion of local breeds, classified as being at risk, not-at-risk or unknown level of risk of extinction At Risk of Extinction 2016 16
PA of Key Biodiversity Areas Coverage (%) Estimate 2016 17
Protected area management effectiveness PA Assessed on Management Effectiveness (%) 2015 18
Protected Area Connectedness Index Protected Area Connectedness Index 2012 19
Species Habitat Index Species Habitat Index 2014 20
Species Protection Index (%) Species Protection Index (%) 2014 21
Species Status Information Index Value 2014 22
Total Wood Removals (roundwood, m3) Total 2014 23
Trends in forest extent (tree cover) Percentage of Tree Cover Loss 2015 24
Nitrogen Deposition Trends (kg N/ha/yr) Nitrogen Deposition Trends (kg N/ha/yr) 2030 25
Trends in Pesticides Use Use of pesticides (3-year average) 2013 26

There were a few instances of duplicated values which were double checked with the original dataset and the erranous value removed. Examples include having two values for USA due to the separation of Hawaii in the original dataset. In these cases Hawaii was removed and the value referring to the rest of the states of the country was used instead. Additionally, Indicator 9, Region-based Marine Trophic Index, the mean of the regions was calculated per country, as countries such as Germany have multiple regions with distinct values.

3.1.2 Other Indicators

A set of other indicators were included in the analysis to expand the coverage of socioeconomic variables. We included the human development index (HDI), average harmonized learning outcomes score, gross domestic product (GDP), corruption perception index (CPI), and population.

These datasets were downloaded, cleaned, and had ISO3 codes added to easily merge them into the analysis. The latest data available was used for each indicator.

3.2 Dataset Compilation

To understand how valuation is spread across geographies, we counted the number of times each country’s ISO code appeared in the corpus for both geography columns added in step 2. The result is the density of studies per country and the density of funding institutions per country for the entire corpus.

The external indicators were also joined onto the dataset to analyze the relationships between these socioeconomic indicators and the density of studies and funding institutions.

The uptake dataset was filtered based on a set of queries listed and explained below. We also calculated the number of times each country’s ISO code appeared with the filtered dataset and joined the external indicators.

  • Q6
  • Q8
  • Q13
  • Q6 OR Q8 OR Q13
  • Q2 ES Valuation
  • Q4
  • Q8 AND Q13
  • Q8 OR Q13

This process was also repeated with an additional filter that excluded any studies published before 2010.

3.4 Pearson Correlations

To summarize this analysis more concisely, we ran a pearson correlation to understand the relationship between the number of studies filtered by _____ and each of the indicators. The results are shown below.

knitr::include_graphics("Outputs/Pearson_correlation_table/correlation_figure.png")
Pearson correlations of geographic valuation studies and indicators

Pearson correlations of geographic valuation studies and indicators 

knitr::include_graphics("Outputs/Pearson_correlation_table/correlation_figure_log.png")
Pearson correlations of log geographic valuation studies and the indicators

Pearson correlations of log geographic valuation studies and the indicators